Overview

Dataset statistics

Number of variables25
Number of observations539040
Missing cells109561
Missing cells (%)0.8%
Duplicate rows591
Duplicate rows (%)0.1%
Total size in memory123.1 MiB
Average record size in memory239.4 B

Variable types

Numeric13
Categorical12

Alerts

Dataset has 591 (0.1%) duplicate rowsDuplicates
month has a high cardinality: 251 distinct valuesHigh cardinality
block has a high cardinality: 2475 distinct valuesHigh cardinality
street_name has a high cardinality: 553 distinct valuesHigh cardinality
resale_price is highly overall correlated with floor_area_sqmHigh correlation
distance_to_mrt_km is highly overall correlated with distance_to_mrt_binsHigh correlation
population_count is highly overall correlated with adult_count and 8 other fieldsHigh correlation
adult_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
children_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
senior_citizen_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
teenager_count is highly overall correlated with population_count and 9 other fieldsHigh correlation
young_adult_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
female_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
male_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
male_female_ratio is highly overall correlated with male_female_ratio_binsHigh correlation
floor_area_sqm is highly overall correlated with resale_price and 2 other fieldsHigh correlation
lease_commence_date is highly overall correlated with floor_area_sqmHigh correlation
distance_to_mrt_bins is highly overall correlated with distance_to_mrt_km and 1 other fieldsHigh correlation
codes_name is highly overall correlated with distance_to_mrt_bins and 1 other fieldsHigh correlation
male_female_ratio_bins is highly overall correlated with teenager_count and 2 other fieldsHigh correlation
population_bins is highly overall correlated with population_count and 9 other fieldsHigh correlation
town is highly overall correlated with population_count and 9 other fieldsHigh correlation
flat_type is highly overall correlated with floor_area_sqm and 1 other fieldsHigh correlation
flat_model is highly overall correlated with flat_typeHigh correlation
mrt_counts is highly imbalanced (89.9%)Imbalance
distance_to_mrt_bins is highly imbalanced (72.6%)Imbalance
male_female_ratio_bins is highly imbalanced (82.8%)Imbalance
resale_price has 107623 (20.0%) missing valuesMissing

Reproduction

Analysis started2023-03-18 03:49:15.225746
Analysis finished2023-03-18 03:50:08.898318
Duration53.67 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

resale_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6404
Distinct (%)1.5%
Missing107623
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean301820.82
Minimum29700
Maximum1123200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:08.982056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum29700
5-th percentile131400
Q1205200
median283950
Q3373500
95-th percentile544500
Maximum1123200
Range1093500
Interquartile range (IQR)168300

Descriptive statistics

Standard deviation129867.22
Coefficient of variation (CV)0.43027921
Kurtosis1.4120769
Mean301820.82
Median Absolute Deviation (MAD)83250
Skewness0.99009256
Sum1.3021063 × 1011
Variance1.6865496 × 1010
MonotonicityNot monotonic
2023-03-18T11:50:09.073288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270000 3773
 
0.7%
315000 3713
 
0.7%
252000 3606
 
0.7%
288000 3604
 
0.7%
360000 3511
 
0.7%
342000 3476
 
0.6%
324000 3418
 
0.6%
297000 3369
 
0.6%
225000 3312
 
0.6%
243000 3161
 
0.6%
Other values (6394) 396474
73.6%
(Missing) 107623
 
20.0%
ValueCountFrequency (%)
29700 1
 
< 0.1%
31500 2
 
< 0.1%
32400 1
 
< 0.1%
33300 3
< 0.1%
34200 3
< 0.1%
36000 3
< 0.1%
36900 3
< 0.1%
37800 2
 
< 0.1%
38700 7
< 0.1%
39150 1
 
< 0.1%
ValueCountFrequency (%)
1123200 1
 
< 0.1%
1108800 1
 
< 0.1%
1087200 2
< 0.1%
1084500 1
 
< 0.1%
1080000 3
< 0.1%
1068199.2 1
 
< 0.1%
1062000 1
 
< 0.1%
1053000 2
< 0.1%
1051200 1
 
< 0.1%
1044799.2 1
 
< 0.1%

distance_to_mrt_km
Real number (ℝ)

Distinct9145
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65271469
Minimum0.022112447
Maximum3.5157764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:09.160378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.022112447
5-th percentile0.16585551
Q10.36758768
median0.58811823
Q30.86051038
95-th percentile1.3991688
Maximum3.5157764
Range3.4936639
Interquartile range (IQR)0.4929227

Descriptive statistics

Standard deviation0.38384447
Coefficient of variation (CV)0.58807389
Kurtosis1.7324037
Mean0.65271469
Median Absolute Deviation (MAD)0.24082632
Skewness1.0947081
Sum351839.33
Variance0.14733658
MonotonicityNot monotonic
2023-03-18T11:50:09.240828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8804880701 413
 
0.1%
0.8806170356 396
 
0.1%
1.009626078 394
 
0.1%
0.9620735662 387
 
0.1%
0.8084054236 385
 
0.1%
0.9636654817 359
 
0.1%
1.213218379 356
 
0.1%
1.170511977 350
 
0.1%
0.6498798611 297
 
0.1%
0.4245036351 292
 
0.1%
Other values (9135) 535411
99.3%
ValueCountFrequency (%)
0.02211244716 80
< 0.1%
0.02648832499 39
< 0.1%
0.03602304855 38
< 0.1%
0.03905292277 4
 
< 0.1%
0.04148001859 10
 
< 0.1%
0.0434819053 36
 
< 0.1%
0.04348866364 39
< 0.1%
0.04372690318 92
< 0.1%
0.04389555737 80
< 0.1%
0.04432644791 94
< 0.1%
ValueCountFrequency (%)
3.515776371 26
 
< 0.1%
3.491570856 86
< 0.1%
3.454113187 30
 
< 0.1%
2.150380755 120
< 0.1%
2.123572473 68
< 0.1%
2.101792667 93
< 0.1%
2.095430751 51
< 0.1%
2.089538192 50
< 0.1%
2.080300714 68
< 0.1%
2.080200347 66
< 0.1%

mrt_counts
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
1
531916 
2
 
7124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

Length

2023-03-18T11:50:09.313316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T11:50:09.382445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 539040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 531916
98.7%
2 7124
 
1.3%

distance_to_mrt_bins
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
491277 
1
 
47621
2
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539040
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

Length

2023-03-18T11:50:09.431292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T11:50:09.488666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 539040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 491277
91.1%
1 47621
 
8.8%
2 142
 
< 0.1%

codes_name
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
NS
168124 
EW
158306 
DT
56264 
NE
48480 
BP
31571 
Other values (9)
76295 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1078080
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEW
2nd rowEW
3rd rowEW
4th rowEW
5th rowEW

Common Values

ValueCountFrequency (%)
NS 168124
31.2%
EW 158306
29.4%
DT 56264
 
10.4%
NE 48480
 
9.0%
BP 31571
 
5.9%
CC 31076
 
5.8%
SE 12642
 
2.3%
SW 12041
 
2.2%
PE 10370
 
1.9%
TE 7726
 
1.4%
Other values (4) 2440
 
0.5%

Length

2023-03-18T11:50:09.750478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ns 168124
31.2%
ew 158306
29.4%
dt 56264
 
10.4%
ne 48480
 
9.0%
bp 31571
 
5.9%
cc 31076
 
5.8%
se 12642
 
2.3%
sw 12041
 
2.2%
pe 10370
 
1.9%
te 7726
 
1.4%
Other values (4) 2440
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E 237524
22.0%
N 216604
20.1%
S 193842
18.0%
W 171448
15.9%
T 65187
 
6.0%
C 62294
 
5.8%
D 56264
 
5.2%
P 43204
 
4.0%
B 31571
 
2.9%
G 142
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1078080
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 237524
22.0%
N 216604
20.1%
S 193842
18.0%
W 171448
15.9%
T 65187
 
6.0%
C 62294
 
5.8%
D 56264
 
5.2%
P 43204
 
4.0%
B 31571
 
2.9%
G 142
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1078080
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 237524
22.0%
N 216604
20.1%
S 193842
18.0%
W 171448
15.9%
T 65187
 
6.0%
C 62294
 
5.8%
D 56264
 
5.2%
P 43204
 
4.0%
B 31571
 
2.9%
G 142
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1078080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 237524
22.0%
N 216604
20.1%
S 193842
18.0%
W 171448
15.9%
T 65187
 
6.0%
C 62294
 
5.8%
D 56264
 
5.2%
P 43204
 
4.0%
B 31571
 
2.9%
G 142
 
< 0.1%

population_count
Real number (ℝ)

Distinct152
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean42463.732
Minimum130
Maximum138490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:09.816607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile10880
Q123940
median33410
Q354880
95-th percentile95530
Maximum138490
Range138360
Interquartile range (IQR)30940

Descriptive statistics

Standard deviation30253.571
Coefficient of variation (CV)0.71245672
Kurtosis2.3946358
Mean42463.732
Median Absolute Deviation (MAD)14910
Skewness1.5857473
Sum2.2881157 × 1010
Variance9.1527858 × 108
MonotonicityNot monotonic
2023-03-18T11:50:09.895768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138490 24506
 
4.5%
95530 20286
 
3.8%
85930 15274
 
2.8%
78150 13356
 
2.5%
57890 12221
 
2.3%
68240 10588
 
2.0%
70930 10064
 
1.9%
56880 9752
 
1.8%
59510 9300
 
1.7%
50130 8995
 
1.7%
Other values (142) 404498
75.0%
ValueCountFrequency (%)
130 46
 
< 0.1%
810 142
< 0.1%
910 89
 
< 0.1%
1210 175
< 0.1%
1490 313
0.1%
1520 174
< 0.1%
1580 246
< 0.1%
2080 321
0.1%
2330 99
 
< 0.1%
2440 215
< 0.1%
ValueCountFrequency (%)
138490 24506
4.5%
95530 20286
3.8%
85930 15274
2.8%
78150 13356
2.5%
70930 10064
1.9%
68240 10588
2.0%
59830 6958
 
1.3%
59510 9300
 
1.7%
57890 12221
2.3%
56880 9752
 
1.8%

adult_count
Real number (ℝ)

Distinct151
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22472.908
Minimum20
Maximum72740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:09.980141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile5920
Q112660
median17440
Q329740
95-th percentile50520
Maximum72740
Range72720
Interquartile range (IQR)17080

Descriptive statistics

Standard deviation15954.547
Coefficient of variation (CV)0.70994581
Kurtosis2.2697044
Mean22472.908
Median Absolute Deviation (MAD)7720
Skewness1.5554882
Sum1.2109302 × 1010
Variance2.5454755 × 108
MonotonicityNot monotonic
2023-03-18T11:50:10.058980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72740 24506
 
4.5%
50520 20286
 
3.8%
45400 15274
 
2.8%
40960 13356
 
2.5%
31660 12221
 
2.3%
37740 10588
 
2.0%
16570 10283
 
1.9%
37240 10064
 
1.9%
30600 9752
 
1.8%
30090 9300
 
1.7%
Other values (141) 403210
74.8%
ValueCountFrequency (%)
20 46
 
< 0.1%
440 142
< 0.1%
490 89
 
< 0.1%
590 175
< 0.1%
800 174
< 0.1%
830 313
0.1%
890 246
< 0.1%
1140 321
0.1%
1180 215
< 0.1%
1250 99
 
< 0.1%
ValueCountFrequency (%)
72740 24506
4.5%
50520 20286
3.8%
45400 15274
2.8%
40960 13356
2.5%
37740 10588
2.0%
37240 10064
1.9%
31990 6958
 
1.3%
31660 12221
2.3%
30600 9752
 
1.8%
30090 9300
 
1.7%

children_count
Real number (ℝ)

Distinct134
Distinct (%)< 0.1%
Missing246
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4147.6129
Minimum50
Maximum12040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.143156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile930
Q12020
median3150
Q36140
95-th percentile11140
Maximum12040
Range11990
Interquartile range (IQR)4120

Descriptive statistics

Standard deviation3023.7938
Coefficient of variation (CV)0.72904436
Kurtosis0.6933565
Mean4147.6129
Median Absolute Deviation (MAD)1630
Skewness1.2138311
Sum2.234709 × 109
Variance9143328.9
MonotonicityNot monotonic
2023-03-18T11:50:10.224355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12040 24506
 
4.5%
11140 20286
 
3.8%
4780 15791
 
2.9%
6960 15274
 
2.8%
2820 14278
 
2.6%
7240 13356
 
2.5%
4830 12221
 
2.3%
9060 10588
 
2.0%
6470 10064
 
1.9%
2330 9798
 
1.8%
Other values (124) 392632
72.8%
ValueCountFrequency (%)
50 142
 
< 0.1%
60 89
 
< 0.1%
70 246
< 0.1%
100 321
0.1%
110 175
 
< 0.1%
120 586
0.1%
200 215
 
< 0.1%
240 490
0.1%
290 408
0.1%
320 87
 
< 0.1%
ValueCountFrequency (%)
12040 24506
4.5%
11140 20286
3.8%
9060 10588
2.0%
7800 7555
 
1.4%
7240 13356
2.5%
7060 3574
 
0.7%
6960 15274
2.8%
6890 6958
 
1.3%
6750 7173
 
1.3%
6740 6243
 
1.2%

senior_citizen_count
Real number (ℝ)

Distinct127
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2595.8679
Minimum50
Maximum8130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.306932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile770
Q11410
median2140
Q33130
95-th percentile7830
Maximum8130
Range8080
Interquartile range (IQR)1720

Descriptive statistics

Standard deviation1817.7502
Coefficient of variation (CV)0.70024759
Kurtosis3.1264898
Mean2595.8679
Median Absolute Deviation (MAD)820
Skewness1.8286929
Sum1.3987574 × 109
Variance3304215.9
MonotonicityNot monotonic
2023-03-18T11:50:10.384754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8130 24506
 
4.5%
2980 20286
 
3.8%
7830 15274
 
2.8%
880 14311
 
2.7%
4260 13356
 
2.5%
2160 12723
 
2.4%
3850 12221
 
2.3%
1410 10370
 
1.9%
2770 10064
 
1.9%
3550 9752
 
1.8%
Other values (117) 395977
73.5%
ValueCountFrequency (%)
50 115
 
< 0.1%
70 89
 
< 0.1%
90 46
 
< 0.1%
100 316
 
0.1%
140 175
 
< 0.1%
190 1059
0.2%
200 87
 
< 0.1%
240 246
 
< 0.1%
260 1364
0.3%
310 100
 
< 0.1%
ValueCountFrequency (%)
8130 24506
4.5%
7830 15274
2.8%
5320 5072
 
0.9%
4260 13356
2.5%
4130 4291
 
0.8%
3980 4014
 
0.7%
3850 12221
2.3%
3770 3729
 
0.7%
3700 4085
 
0.8%
3580 3940
 
0.7%

teenager_count
Real number (ℝ)

Distinct137
Distinct (%)< 0.1%
Missing246
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5233.5191
Minimum80
Maximum16780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.467941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile1030
Q12470
median3950
Q36480
95-th percentile14940
Maximum16780
Range16700
Interquartile range (IQR)4010

Descriptive statistics

Standard deviation4059.6597
Coefficient of variation (CV)0.77570361
Kurtosis1.5546525
Mean5233.5191
Median Absolute Deviation (MAD)2030
Skewness1.4393882
Sum2.8197887 × 109
Variance16480837
MonotonicityNot monotonic
2023-03-18T11:50:10.547145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16780 24506
 
4.5%
14940 20286
 
3.8%
9100 15274
 
2.8%
9730 13356
 
2.5%
6480 12221
 
2.3%
8320 10588
 
2.0%
10130 10064
 
1.9%
6730 9752
 
1.8%
8970 9300
 
1.7%
5360 8995
 
1.7%
Other values (127) 404452
75.0%
ValueCountFrequency (%)
80 335
0.1%
90 142
 
< 0.1%
110 313
0.1%
120 496
0.1%
140 99
 
< 0.1%
160 174
 
< 0.1%
240 215
< 0.1%
270 408
0.1%
340 490
0.1%
380 115
 
< 0.1%
ValueCountFrequency (%)
16780 24506
4.5%
14940 20286
3.8%
10130 10064
1.9%
9730 13356
2.5%
9100 15274
2.8%
8970 9300
 
1.7%
8320 10588
2.0%
8210 6958
 
1.3%
7420 7173
 
1.3%
6730 9752
 
1.8%

young_adult_count
Real number (ℝ)

Distinct134
Distinct (%)< 0.1%
Missing246
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6165.5605
Minimum80
Maximum22580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.630241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile1240
Q13170
median4610
Q38140
95-th percentile13590
Maximum22580
Range22500
Interquartile range (IQR)4970

Descriptive statistics

Standard deviation4876.7097
Coefficient of variation (CV)0.79095967
Kurtosis3.3882877
Mean6165.5605
Median Absolute Deviation (MAD)2110
Skewness1.7927531
Sum3.321967 × 109
Variance23782297
MonotonicityNot monotonic
2023-03-18T11:50:10.709099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22580 24506
 
4.5%
13590 20286
 
3.8%
11290 15274
 
2.8%
12630 13356
 
2.5%
8480 12221
 
2.3%
5320 11332
 
2.1%
8810 10588
 
2.0%
2290 10492
 
1.9%
11690 10064
 
1.9%
5220 9855
 
1.8%
Other values (124) 400820
74.4%
ValueCountFrequency (%)
80 142
 
< 0.1%
150 823
0.2%
220 321
 
0.1%
240 99
 
< 0.1%
270 174
 
< 0.1%
290 215
 
< 0.1%
320 408
0.1%
390 100
 
< 0.1%
440 605
0.1%
460 412
0.1%
ValueCountFrequency (%)
22580 24506
4.5%
13590 20286
3.8%
12630 13356
2.5%
11690 10064
1.9%
11290 15274
2.8%
10450 9300
 
1.7%
8810 10588
2.0%
8620 7555
 
1.4%
8480 12221
2.3%
8440 8995
 
1.7%

female_count
Real number (ℝ)

Distinct146
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21490.697
Minimum80
Maximum70600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.789960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile5600
Q112470
median16840
Q327770
95-th percentile47670
Maximum70600
Range70520
Interquartile range (IQR)15300

Descriptive statistics

Standard deviation15300.608
Coefficient of variation (CV)0.71196428
Kurtosis2.5725574
Mean21490.697
Median Absolute Deviation (MAD)7420
Skewness1.6242185
Sum1.1580047 × 1010
Variance2.3410862 × 108
MonotonicityNot monotonic
2023-03-18T11:50:10.867347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70600 24506
 
4.5%
47670 20286
 
3.8%
43950 15274
 
2.8%
39520 13356
 
2.5%
29080 12221
 
2.3%
17890 11402
 
2.1%
33940 10588
 
2.0%
34890 10064
 
1.9%
28490 9752
 
1.8%
29980 9300
 
1.7%
Other values (136) 402091
74.6%
ValueCountFrequency (%)
80 46
 
< 0.1%
390 142
< 0.1%
450 89
 
< 0.1%
640 175
< 0.1%
760 174
< 0.1%
780 313
0.1%
820 246
< 0.1%
1090 321
0.1%
1200 215
< 0.1%
1220 99
 
< 0.1%
ValueCountFrequency (%)
70600 24506
4.5%
47670 20286
3.8%
43950 15274
2.8%
39520 13356
2.5%
34890 10064
1.9%
33940 10588
2.0%
30240 6958
 
1.3%
29980 9300
 
1.7%
29080 12221
2.3%
28490 9752
 
1.8%

male_count
Real number (ℝ)

Distinct145
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20973.036
Minimum50
Maximum67890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:10.965883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile5380
Q111920
median16320
Q327200
95-th percentile47860
Maximum67890
Range67840
Interquartile range (IQR)15280

Descriptive statistics

Standard deviation14961.131
Coefficient of variation (CV)0.71335078
Kurtosis2.2136608
Mean20973.036
Median Absolute Deviation (MAD)7240
Skewness1.5460354
Sum1.1301110 × 1010
Variance2.2383545 × 108
MonotonicityNot monotonic
2023-03-18T11:50:11.061590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67890 24506
 
4.5%
47860 20286
 
3.8%
41980 15274
 
2.8%
38630 13356
 
2.5%
17960 12465
 
2.3%
28810 12221
 
2.3%
34300 10588
 
2.0%
36040 10064
 
1.9%
28390 9752
 
1.8%
29530 9300
 
1.7%
Other values (135) 401028
74.4%
ValueCountFrequency (%)
50 46
 
< 0.1%
420 142
 
< 0.1%
460 89
 
< 0.1%
570 175
< 0.1%
710 313
0.1%
760 420
0.1%
990 321
0.1%
1110 99
 
< 0.1%
1240 215
< 0.1%
1410 115
 
< 0.1%
ValueCountFrequency (%)
67890 24506
4.5%
47860 20286
3.8%
41980 15274
2.8%
38630 13356
2.5%
36040 10064
1.9%
34300 10588
2.0%
29590 6958
 
1.3%
29530 9300
 
1.7%
28810 12221
2.3%
28390 9752
 
1.8%

male_female_ratio
Real number (ℝ)

Distinct154
Distinct (%)< 0.1%
Missing200
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.9714201
Minimum0.625
Maximum1.2362385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:11.152794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.625
5-th percentile0.89095128
Q10.95517634
median0.97747976
Q30.99649
95-th percentile1.0329607
Maximum1.2362385
Range0.61123853
Interquartile range (IQR)0.04131366

Descriptive statistics

Standard deviation0.040533059
Coefficient of variation (CV)0.041725572
Kurtosis3.0815801
Mean0.9714201
Median Absolute Deviation (MAD)0.022153297
Skewness-0.19392216
Sum523440.01
Variance0.0016429289
MonotonicityNot monotonic
2023-03-18T11:50:11.232828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9616147309 24506
 
4.5%
1.003985735 20286
 
3.8%
0.9551763367 15274
 
2.8%
0.9774797571 13356
 
2.5%
0.9907152682 12221
 
2.3%
1.010606953 10588
 
2.0%
1.032960734 10064
 
1.9%
0.9964899965 9752
 
1.8%
0.9849899933 9300
 
1.7%
0.9775147929 8995
 
1.7%
Other values (144) 404498
75.0%
ValueCountFrequency (%)
0.625 46
 
< 0.1%
0.8447606727 2550
0.5%
0.854368932 2216
0.4%
0.8550295858 213
 
< 0.1%
0.8668639053 673
 
0.1%
0.8670212766 100
 
< 0.1%
0.8756218905 854
 
0.2%
0.8771121352 1278
 
0.2%
0.877842755 4085
0.8%
0.88 412
 
0.1%
ValueCountFrequency (%)
1.236238532 592
 
0.1%
1.174603175 318
 
0.1%
1.115384615 490
 
0.1%
1.103869654 634
 
0.1%
1.086092715 408
 
0.1%
1.076923077 142
 
< 0.1%
1.051417271 4536
0.8%
1.05046729 5598
1.0%
1.04 1364
 
0.3%
1.038997214 3041
0.6%

male_female_ratio_bins
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
1
505107 
2
 
33687
3
 
200
0
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539040
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

Length

2023-03-18T11:50:11.425413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T11:50:11.493657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 539040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 505107
93.7%
2 33687
 
6.2%
3 200
 
< 0.1%
0 46
 
< 0.1%

population_bins
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
0
377740 
1
116308 
2
44792 
3
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters539040
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

Length

2023-03-18T11:50:11.563092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T11:50:11.631301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 539040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 377740
70.1%
1 116308
 
21.6%
2 44792
 
8.3%
3 200
 
< 0.1%

month
Categorical

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
2010-07
 
3651
2010-06
 
3499
2001-10
 
3449
2009-10
 
3414
2001-07
 
3337
Other values (246)
521690 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3773280
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2001-08
2nd row2002-09
3rd row2020-10
4th row2010-10
5th row2002-08

Common Values

ValueCountFrequency (%)
2010-07 3651
 
0.7%
2010-06 3499
 
0.6%
2001-10 3449
 
0.6%
2009-10 3414
 
0.6%
2001-07 3337
 
0.6%
2002-01 3329
 
0.6%
2001-11 3289
 
0.6%
2001-06 3282
 
0.6%
2010-08 3278
 
0.6%
2000-11 3264
 
0.6%
Other values (241) 505248
93.7%

Length

2023-03-18T11:50:11.691159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-07 3651
 
0.7%
2010-06 3499
 
0.6%
2001-10 3449
 
0.6%
2009-10 3414
 
0.6%
2001-07 3337
 
0.6%
2002-01 3329
 
0.6%
2001-11 3289
 
0.6%
2001-06 3282
 
0.6%
2010-08 3278
 
0.6%
2000-11 3264
 
0.6%
Other values (241) 505248
93.7%

Most occurring characters

ValueCountFrequency (%)
0 1387290
36.8%
2 695849
18.4%
- 539040
 
14.3%
1 495528
 
13.1%
9 98918
 
2.6%
7 95938
 
2.5%
8 95520
 
2.5%
6 93540
 
2.5%
5 91911
 
2.4%
3 91099
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3234240
85.7%
Dash Punctuation 539040
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1387290
42.9%
2 695849
21.5%
1 495528
 
15.3%
9 98918
 
3.1%
7 95938
 
3.0%
8 95520
 
3.0%
6 93540
 
2.9%
5 91911
 
2.8%
3 91099
 
2.8%
4 88647
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 539040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3773280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1387290
36.8%
2 695849
18.4%
- 539040
 
14.3%
1 495528
 
13.1%
9 98918
 
2.6%
7 95938
 
2.5%
8 95520
 
2.5%
6 93540
 
2.5%
5 91911
 
2.4%
3 91099
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3773280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1387290
36.8%
2 695849
18.4%
- 539040
 
14.3%
1 495528
 
13.1%
9 98918
 
2.6%
7 95938
 
2.5%
8 95520
 
2.5%
6 93540
 
2.5%
5 91911
 
2.4%
3 91099
 
2.4%

town
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
woodlands
48807 
tampines
42733 
jurong west
41019 
yishun
35734 
bedok
34440 
Other values (21)
336307 

Length

Max length15
Median length12
Mean length9.1008886
Min length5

Characters and Unicode

Total characters4905743
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpasir ris
2nd rowpasir ris
3rd rowpasir ris
4th rowpasir ris
5th rowpasir ris

Common Values

ValueCountFrequency (%)
woodlands 48807
 
9.1%
tampines 42733
 
7.9%
jurong west 41019
 
7.6%
yishun 35734
 
6.6%
bedok 34440
 
6.4%
hougang 29776
 
5.5%
ang mo kio 27778
 
5.2%
choa chu kang 24882
 
4.6%
sengkang 24215
 
4.5%
bukit batok 24172
 
4.5%
Other values (16) 205484
38.1%

Length

2023-03-18T11:50:11.757273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bukit 64055
 
7.9%
jurong 54762
 
6.8%
woodlands 48807
 
6.0%
tampines 42733
 
5.3%
west 41019
 
5.1%
yishun 35734
 
4.4%
bedok 34440
 
4.3%
hougang 29776
 
3.7%
kio 27778
 
3.4%
mo 27778
 
3.4%
Other values (26) 401372
49.7%

Most occurring characters

ValueCountFrequency (%)
a 515185
 
10.5%
n 493635
 
10.1%
o 424314
 
8.6%
g 327661
 
6.7%
s 296059
 
6.0%
e 290393
 
5.9%
269214
 
5.5%
i 240269
 
4.9%
t 238477
 
4.9%
u 237682
 
4.8%
Other values (14) 1572854
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4622594
94.2%
Space Separator 269214
 
5.5%
Other Punctuation 13935
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 515185
 
11.1%
n 493635
 
10.7%
o 424314
 
9.2%
g 327661
 
7.1%
s 296059
 
6.4%
e 290393
 
6.3%
i 240269
 
5.2%
t 238477
 
5.2%
u 237682
 
5.1%
k 213477
 
4.6%
Other values (12) 1345442
29.1%
Space Separator
ValueCountFrequency (%)
269214
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 13935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4622594
94.2%
Common 283149
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 515185
 
11.1%
n 493635
 
10.7%
o 424314
 
9.2%
g 327661
 
7.1%
s 296059
 
6.4%
e 290393
 
6.3%
i 240269
 
5.2%
t 238477
 
5.2%
u 237682
 
5.1%
k 213477
 
4.6%
Other values (12) 1345442
29.1%
Common
ValueCountFrequency (%)
269214
95.1%
/ 13935
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4905743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 515185
 
10.5%
n 493635
 
10.1%
o 424314
 
8.6%
g 327661
 
6.7%
s 296059
 
6.0%
e 290393
 
5.9%
269214
 
5.5%
i 240269
 
4.9%
t 238477
 
4.9%
u 237682
 
4.8%
Other values (14) 1572854
32.1%

flat_type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
4-room
211801 
3-room
156929 
5-room
123141 
executive
40922 
2-room
 
5664
Other values (2)
 
583

Length

Max length16
Median length6
Mean length6.2319605
Min length6

Characters and Unicode

Total characters3359276
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4-room
2nd row4-room
3rd rowexecutive
4th row4-room
5th row4-room

Common Values

ValueCountFrequency (%)
4-room 211801
39.3%
3-room 156929
29.1%
5-room 123141
22.8%
executive 40922
 
7.6%
2-room 5664
 
1.1%
1-room 356
 
0.1%
multi-generation 227
 
< 0.1%

Length

2023-03-18T11:50:11.833859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T11:50:11.910148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4-room 211801
39.3%
3-room 156929
29.1%
5-room 123141
22.8%
executive 40922
 
7.6%
2-room 5664
 
1.1%
1-room 356
 
0.1%
multi-generation 227
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 996009
29.6%
- 498118
14.8%
r 498118
14.8%
m 498118
14.8%
4 211801
 
6.3%
3 156929
 
4.7%
e 123220
 
3.7%
5 123141
 
3.7%
i 41376
 
1.2%
t 41376
 
1.2%
Other values (10) 171070
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2363267
70.4%
Dash Punctuation 498118
 
14.8%
Decimal Number 497891
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 996009
42.1%
r 498118
21.1%
m 498118
21.1%
e 123220
 
5.2%
i 41376
 
1.8%
t 41376
 
1.8%
u 41149
 
1.7%
c 40922
 
1.7%
x 40922
 
1.7%
v 40922
 
1.7%
Other values (4) 1135
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 211801
42.5%
3 156929
31.5%
5 123141
24.7%
2 5664
 
1.1%
1 356
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 498118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2363267
70.4%
Common 996009
29.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 996009
42.1%
r 498118
21.1%
m 498118
21.1%
e 123220
 
5.2%
i 41376
 
1.8%
t 41376
 
1.8%
u 41149
 
1.7%
c 40922
 
1.7%
x 40922
 
1.7%
v 40922
 
1.7%
Other values (4) 1135
 
< 0.1%
Common
ValueCountFrequency (%)
- 498118
50.0%
4 211801
21.3%
3 156929
 
15.8%
5 123141
 
12.4%
2 5664
 
0.6%
1 356
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3359276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 996009
29.6%
- 498118
14.8%
r 498118
14.8%
m 498118
14.8%
4 211801
 
6.3%
3 156929
 
4.7%
e 123220
 
3.7%
5 123141
 
3.7%
i 41376
 
1.2%
t 41376
 
1.2%
Other values (10) 171070
 
5.1%

block
Categorical

Distinct2475
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
1
 
2097
2
 
2052
101
 
1776
4
 
1764
113
 
1764
Other values (2470)
529587 

Length

Max length4
Median length3
Mean length2.9713045
Min length1

Characters and Unicode

Total characters1601652
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row440
2nd row473
3rd row220
4th row474
5th row751

Common Values

ValueCountFrequency (%)
1 2097
 
0.4%
2 2052
 
0.4%
101 1776
 
0.3%
4 1764
 
0.3%
113 1764
 
0.3%
110 1751
 
0.3%
114 1745
 
0.3%
108 1711
 
0.3%
107 1706
 
0.3%
8 1659
 
0.3%
Other values (2465) 521015
96.7%

Length

2023-03-18T11:50:11.986177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 2097
 
0.4%
2 2052
 
0.4%
101 1776
 
0.3%
113 1764
 
0.3%
4 1764
 
0.3%
110 1751
 
0.3%
114 1745
 
0.3%
108 1711
 
0.3%
107 1706
 
0.3%
8 1659
 
0.3%
Other values (2465) 521015
96.7%

Most occurring characters

ValueCountFrequency (%)
1 234732
14.7%
2 200947
12.5%
3 169824
10.6%
4 163842
10.2%
6 153148
9.6%
5 150386
9.4%
7 128100
8.0%
0 120763
7.5%
8 111731
7.0%
9 86758
 
5.4%
Other values (11) 81421
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1520231
94.9%
Uppercase Letter 81421
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 26660
32.7%
B 22912
28.1%
C 17318
21.3%
D 10195
 
12.5%
E 1974
 
2.4%
F 1025
 
1.3%
G 737
 
0.9%
H 320
 
0.4%
J 156
 
0.2%
M 68
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 234732
15.4%
2 200947
13.2%
3 169824
11.2%
4 163842
10.8%
6 153148
10.1%
5 150386
9.9%
7 128100
8.4%
0 120763
7.9%
8 111731
7.3%
9 86758
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1520231
94.9%
Latin 81421
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 26660
32.7%
B 22912
28.1%
C 17318
21.3%
D 10195
 
12.5%
E 1974
 
2.4%
F 1025
 
1.3%
G 737
 
0.9%
H 320
 
0.4%
J 156
 
0.2%
M 68
 
0.1%
Common
ValueCountFrequency (%)
1 234732
15.4%
2 200947
13.2%
3 169824
11.2%
4 163842
10.8%
6 153148
10.1%
5 150386
9.9%
7 128100
8.4%
0 120763
7.9%
8 111731
7.3%
9 86758
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1601652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 234732
14.7%
2 200947
12.5%
3 169824
10.6%
4 163842
10.2%
6 153148
9.6%
5 150386
9.4%
7 128100
8.0%
0 120763
7.5%
8 111731
7.0%
9 86758
 
5.4%
Other values (11) 81421
 
5.1%

street_name
Categorical

Distinct553
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
yishun ring road
 
9152
ang mo kio avenue 10
 
7342
bedok reservoir road
 
7095
ang mo kio avenue 3
 
6383
hougang avenue 8
 
5127
Other values (548)
503941 

Length

Max length25
Median length22
Mean length17.301354
Min length9

Characters and Unicode

Total characters9326122
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpasir ris drive 4
2nd rowpasir ris drive 6
3rd rowpasir ris street 21
4th rowpasir ris drive 6
5th rowpasir ris street 71

Common Values

ValueCountFrequency (%)
yishun ring road 9152
 
1.7%
ang mo kio avenue 10 7342
 
1.4%
bedok reservoir road 7095
 
1.3%
ang mo kio avenue 3 6383
 
1.2%
hougang avenue 8 5127
 
1.0%
bedok north street 3 4032
 
0.7%
ang mo kio avenue 4 3970
 
0.7%
tampines street 21 3904
 
0.7%
bedok north road 3712
 
0.7%
woodlands ring road 3652
 
0.7%
Other values (543) 484671
89.9%

Length

2023-03-18T11:50:12.062236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street 163706
 
9.6%
avenue 126471
 
7.4%
road 90863
 
5.3%
drive 47949
 
2.8%
west 45046
 
2.6%
woodlands 42888
 
2.5%
jurong 40593
 
2.4%
tampines 37862
 
2.2%
yishun 35734
 
2.1%
bukit 31096
 
1.8%
Other values (307) 1051865
61.4%

Most occurring characters

ValueCountFrequency (%)
1175033
12.6%
e 1038593
 
11.1%
a 745442
 
8.0%
n 651385
 
7.0%
t 626141
 
6.7%
r 592318
 
6.4%
o 579759
 
6.2%
s 508302
 
5.5%
i 377976
 
4.1%
u 318772
 
3.4%
Other values (27) 2712401
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7662523
82.2%
Space Separator 1175033
 
12.6%
Decimal Number 487601
 
5.2%
Other Punctuation 965
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1038593
13.6%
a 745442
 
9.7%
n 651385
 
8.5%
t 626141
 
8.2%
r 592318
 
7.7%
o 579759
 
7.6%
s 508302
 
6.6%
i 377976
 
4.9%
u 318772
 
4.2%
d 283540
 
3.7%
Other values (15) 1940295
25.3%
Decimal Number
ValueCountFrequency (%)
1 134482
27.6%
2 84657
17.4%
4 58659
12.0%
3 58114
11.9%
5 45182
 
9.3%
6 31842
 
6.5%
8 25426
 
5.2%
7 21986
 
4.5%
0 16466
 
3.4%
9 10787
 
2.2%
Space Separator
ValueCountFrequency (%)
1175033
100.0%
Other Punctuation
ValueCountFrequency (%)
' 965
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7662523
82.2%
Common 1663599
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1038593
13.6%
a 745442
 
9.7%
n 651385
 
8.5%
t 626141
 
8.2%
r 592318
 
7.7%
o 579759
 
7.6%
s 508302
 
6.6%
i 377976
 
4.9%
u 318772
 
4.2%
d 283540
 
3.7%
Other values (15) 1940295
25.3%
Common
ValueCountFrequency (%)
1175033
70.6%
1 134482
 
8.1%
2 84657
 
5.1%
4 58659
 
3.5%
3 58114
 
3.5%
5 45182
 
2.7%
6 31842
 
1.9%
8 25426
 
1.5%
7 21986
 
1.3%
0 16466
 
1.0%
Other values (2) 11752
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9326122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1175033
12.6%
e 1038593
 
11.1%
a 745442
 
8.0%
n 651385
 
7.0%
t 626141
 
6.7%
r 592318
 
6.4%
o 579759
 
6.2%
s 508302
 
5.5%
i 377976
 
4.1%
u 318772
 
3.4%
Other values (27) 2712401
29.1%

storey_range
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
04 to 06
134068 
07 to 09
119555 
01 to 03
107840 
10 to 12
101653 
13 to 15
38554 
Other values (20)
37370 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4312320
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01 to 03
2nd row04 to 06
3rd row10 to 12
4th row04 to 06
5th row04 to 06

Common Values

ValueCountFrequency (%)
04 to 06 134068
24.9%
07 to 09 119555
22.2%
01 to 03 107840
20.0%
10 to 12 101653
18.9%
13 to 15 38554
 
7.2%
16 to 18 15166
 
2.8%
19 to 21 6870
 
1.3%
22 to 24 4380
 
0.8%
01 to 05 2693
 
0.5%
06 to 10 2466
 
0.5%
Other values (15) 5795
 
1.1%

Length

2023-03-18T11:50:12.132745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 539040
33.3%
06 136534
 
8.4%
04 134068
 
8.3%
07 119555
 
7.4%
09 119555
 
7.4%
01 110533
 
6.8%
03 107840
 
6.7%
10 104119
 
6.4%
12 101653
 
6.3%
15 39806
 
2.5%
Other values (30) 104417
 
6.5%

Most occurring characters

ValueCountFrequency (%)
1078080
25.0%
0 836424
19.4%
t 539040
12.5%
o 539040
12.5%
1 441891
10.2%
6 152314
 
3.5%
3 149495
 
3.5%
4 139108
 
3.2%
2 127433
 
3.0%
9 126708
 
2.9%
Other values (3) 182787
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156160
50.0%
Space Separator 1078080
25.0%
Lowercase Letter 1078080
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836424
38.8%
1 441891
20.5%
6 152314
 
7.1%
3 149495
 
6.9%
4 139108
 
6.5%
2 127433
 
5.9%
9 126708
 
5.9%
7 121862
 
5.7%
5 44655
 
2.1%
8 16270
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
t 539040
50.0%
o 539040
50.0%
Space Separator
ValueCountFrequency (%)
1078080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234240
75.0%
Latin 1078080
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1078080
33.3%
0 836424
25.9%
1 441891
13.7%
6 152314
 
4.7%
3 149495
 
4.6%
4 139108
 
4.3%
2 127433
 
3.9%
9 126708
 
3.9%
7 121862
 
3.8%
5 44655
 
1.4%
Latin
ValueCountFrequency (%)
t 539040
50.0%
o 539040
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4312320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1078080
25.0%
0 836424
19.4%
t 539040
12.5%
o 539040
12.5%
1 441891
10.2%
6 152314
 
3.5%
3 149495
 
3.5%
4 139108
 
3.2%
2 127433
 
3.0%
9 126708
 
2.9%
Other values (3) 182787
 
4.2%

floor_area_sqm
Real number (ℝ)

Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.972538
Minimum31
Maximum297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:12.207755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile62
Q174
median99
Q3114
95-th percentile144
Maximum297
Range266
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.189019
Coefficient of variation (CV)0.25975415
Kurtosis-0.31237585
Mean96.972538
Median Absolute Deviation (MAD)21
Skewness0.29734772
Sum52272077
Variance634.48668
MonotonicityNot monotonic
2023-03-18T11:50:12.304030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 36232
 
6.7%
104 26226
 
4.9%
110 21024
 
3.9%
68 20876
 
3.9%
84 18640
 
3.5%
121 17135
 
3.2%
91 16332
 
3.0%
92 15953
 
3.0%
73 15425
 
2.9%
103 15317
 
2.8%
Other values (179) 335880
62.3%
ValueCountFrequency (%)
31 356
0.1%
34 69
 
< 0.1%
35 20
 
< 0.1%
37 2
 
< 0.1%
38 19
 
< 0.1%
39 26
 
< 0.1%
40 251
< 0.1%
41 68
 
< 0.1%
42 430
0.1%
43 262
< 0.1%
ValueCountFrequency (%)
297 1
 
< 0.1%
280 3
< 0.1%
266 4
< 0.1%
261 3
< 0.1%
259 2
 
< 0.1%
250 1
 
< 0.1%
249 3
< 0.1%
243 5
< 0.1%
241 4
< 0.1%
239 2
 
< 0.1%

flat_model
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
model a
159248 
improved
142224 
new generation
97840 
premium apartment
35553 
simplified
30780 
Other values (15)
73395 

Length

Max length22
Median length19
Mean length9.6175052
Min length4

Characters and Unicode

Total characters5184220
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmodel a
2nd rowmodel a
3rd rowapartment
4th rowmodel a
5th rowmodel a

Common Values

ValueCountFrequency (%)
model a 159248
29.5%
improved 142224
26.4%
new generation 97840
18.2%
premium apartment 35553
 
6.6%
simplified 30780
 
5.7%
apartment 22295
 
4.1%
standard 22170
 
4.1%
maisonette 14859
 
2.8%
model a2 9131
 
1.7%
dbss 1725
 
0.3%
Other values (10) 3215
 
0.6%

Length

2023-03-18T11:50:12.429858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model 169291
20.0%
a 160160
19.0%
improved 142294
16.8%
generation 98067
11.6%
new 97840
11.6%
apartment 57884
 
6.9%
premium 35671
 
4.2%
simplified 30780
 
3.6%
standard 22170
 
2.6%
maisonette 15923
 
1.9%
Other values (12) 14497
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 764006
14.7%
m 487761
9.4%
a 445899
8.6%
o 426707
8.2%
n 391007
7.5%
d 390542
7.5%
i 385578
7.4%
r 356902
 
6.9%
305537
 
5.9%
t 269982
 
5.2%
Other values (14) 960299
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4869118
93.9%
Space Separator 305537
 
5.9%
Decimal Number 9565
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 764006
15.7%
m 487761
10.0%
a 445899
9.2%
o 426707
8.8%
n 391007
8.0%
d 390542
8.0%
i 385578
7.9%
r 356902
7.3%
t 269982
 
5.5%
p 267043
 
5.5%
Other values (11) 683691
14.0%
Decimal Number
ValueCountFrequency (%)
2 9287
97.1%
1 278
 
2.9%
Space Separator
ValueCountFrequency (%)
305537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4869118
93.9%
Common 315102
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 764006
15.7%
m 487761
10.0%
a 445899
9.2%
o 426707
8.8%
n 391007
8.0%
d 390542
8.0%
i 385578
7.9%
r 356902
7.3%
t 269982
 
5.5%
p 267043
 
5.5%
Other values (11) 683691
14.0%
Common
ValueCountFrequency (%)
305537
97.0%
2 9287
 
2.9%
1 278
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5184220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 764006
14.7%
m 487761
9.4%
a 445899
8.6%
o 426707
8.2%
n 391007
7.5%
d 390542
7.5%
i 385578
7.4%
r 356902
 
6.9%
305537
 
5.9%
t 269982
 
5.2%
Other values (14) 960299
18.5%

lease_commence_date
Real number (ℝ)

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989.4147
Minimum1966
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 MiB
2023-03-18T11:50:12.521636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1966
5-th percentile1974
Q11983
median1988
Q31997
95-th percentile2005
Maximum2019
Range53
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.256274
Coefficient of variation (CV)0.0051554227
Kurtosis-0.45109548
Mean1989.4147
Median Absolute Deviation (MAD)8
Skewness0.18848978
Sum1.0723741 × 109
Variance105.19115
MonotonicityNot monotonic
2023-03-18T11:50:12.606586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1985 43376
 
8.0%
1984 31326
 
5.8%
1997 28346
 
5.3%
1988 26176
 
4.9%
1996 23782
 
4.4%
1998 23268
 
4.3%
1987 21670
 
4.0%
1978 20105
 
3.7%
1986 19205
 
3.6%
1999 18449
 
3.4%
Other values (44) 283337
52.6%
ValueCountFrequency (%)
1966 28
 
< 0.1%
1967 3773
0.7%
1968 1018
 
0.2%
1969 3944
0.7%
1970 6272
1.2%
1971 4076
0.8%
1972 3113
 
0.6%
1973 4423
0.8%
1974 6966
1.3%
1975 8954
1.7%
ValueCountFrequency (%)
2019 3
 
< 0.1%
2018 2
 
< 0.1%
2017 56
 
< 0.1%
2016 1676
 
0.3%
2015 5227
1.0%
2014 1958
 
0.4%
2013 3515
0.7%
2012 3350
0.6%
2011 1813
 
0.3%
2010 1025
 
0.2%

Interactions

2023-03-18T11:50:01.430324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:40.451063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.131226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.890345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.741917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.512005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.173431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.094128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.768100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.482101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.158228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.932294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.691591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.681888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:40.579042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.264271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.020065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.872674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.638477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.302729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.226949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.890319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.611040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.286264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.056722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.828987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.820017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:40.701557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.402654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.158041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.005012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.768887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.536660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.361174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.015183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.741221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.420790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.183179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.967284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.956517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:40.824972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.546118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.289344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.136934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.899719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.670166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.490457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.140403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.870765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.551693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.309435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.100807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.090259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:40.944989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.680310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.418872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.268513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.029439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.812631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.619328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.264027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.998923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.685121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.437203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.244663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.224818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.066423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.811655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.551435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.406222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.157176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.050381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.745506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.385711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.130127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.814180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.565271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.377287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.369011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.189908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.958864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.685396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.605907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.286120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.187995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:51.878222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.617090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.260823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.944266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.695861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.514441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.505924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.419567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.095124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.820052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.748946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.414586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.318003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.006157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.743651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.389335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.072948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.826783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.648694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.649677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.535487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.225372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:44.950467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:46.874973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.536784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.448090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.129896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.862817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.511889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.196926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:58.953292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.776667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.788152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.656213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.362106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.093781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.006753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.663709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.578367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.257937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:53.987200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.642773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.328048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.080650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:00.908566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:02.931720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.774980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.499370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.229231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.136502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.793682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.707217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.385707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.114283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.774106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.555655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.209506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.040988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:03.066647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:41.893799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.633668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.480083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.265406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:48.926913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.839351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.518034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.243061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:55.902136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.685090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.336224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.172425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:03.209629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:42.011452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:43.771206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:45.619299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:47.396889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:49.057343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:50.975816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:52.650869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:54.368154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:56.032847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:57.817534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:49:59.540817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-18T11:50:01.308255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-18T11:50:12.692091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
resale_pricedistance_to_mrt_kmpopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiofloor_area_sqmlease_commence_datemrt_countsdistance_to_mrt_binscodes_namemale_female_ratio_binspopulation_binstownflat_typestorey_rangeflat_model
resale_price1.000-0.1070.0190.0180.094-0.1020.021-0.0080.0220.014-0.0610.5960.4380.0650.0250.0930.0440.0750.1470.2780.1780.254
distance_to_mrt_km-0.1071.0000.0540.042-0.0310.1330.0550.0690.0520.0510.0490.003-0.1770.0880.9310.4230.1550.1270.3660.0640.0380.105
population_count0.0190.0541.0000.9980.9440.7210.9670.9700.9990.9990.2530.2170.2410.1110.1020.3060.2490.8860.6010.1010.0660.171
adult_count0.0180.0420.9981.0000.9450.7170.9620.9660.9980.9980.2530.2110.2460.1150.1090.3360.2040.8920.6140.1030.0650.168
children_count0.094-0.0310.9440.9451.0000.5560.9200.8930.9400.9430.2830.2540.3930.1310.1750.3100.2540.8750.5970.1350.0630.187
senior_citizen_count-0.1020.1330.7210.7170.5561.0000.6070.6660.7360.712-0.182-0.093-0.2620.1090.1040.2840.1650.5780.5310.0900.0490.141
teenager_count0.0210.0550.9670.9620.9200.6071.0000.9730.9620.9700.3520.2870.2980.1260.1230.3710.5570.9290.6270.1410.0780.189
young_adult_count-0.0080.0690.9700.9660.8930.6660.9731.0000.9650.9730.3300.2490.2350.1500.0810.3100.3590.9810.6410.1290.0700.188
female_count0.0220.0520.9990.9980.9400.7360.9620.9651.0000.9970.2270.2120.2320.1150.1020.3130.1480.8900.6230.0990.0670.167
male_count0.0140.0510.9990.9980.9430.7120.9700.9730.9971.0000.2780.2180.2440.1230.1130.3180.2070.9650.6350.1320.0670.200
male_female_ratio-0.0610.0490.2530.2530.283-0.1820.3520.3300.2270.2781.0000.2210.3540.0750.2490.2380.7740.2260.4820.1020.0500.136
floor_area_sqm0.5960.0030.2170.2110.254-0.0930.2870.2490.2120.2180.2211.0000.5000.0830.0630.0970.0370.0810.1960.6790.0550.444
lease_commence_date0.438-0.1770.2410.2460.393-0.2620.2980.2350.2320.2440.3540.5001.0000.1480.1210.2450.0770.1930.4530.2890.1350.408
mrt_counts0.0650.0880.1110.1150.1310.1090.1260.1500.1150.1230.0750.0830.1481.0000.0360.4310.0300.0350.2960.0750.0350.090
distance_to_mrt_bins0.0250.9310.1020.1090.1750.1040.1230.0810.1020.1130.2490.0630.1210.0361.0000.7220.1160.0680.3360.0800.0250.090
codes_name0.0930.4230.3060.3360.3100.2840.3710.3100.3130.3180.2380.0970.2450.4310.7221.0000.1220.2980.6190.1010.0590.139
male_female_ratio_bins0.0440.1550.2490.2040.2540.1650.5570.3590.1480.2070.7740.0370.0770.0300.1160.1221.0000.5800.2930.0180.0200.071
population_bins0.0750.1270.8860.8920.8750.5780.9290.9810.8900.9650.2260.0810.1930.0350.0680.2980.5801.0000.5540.0710.0740.141
town0.1470.3660.6010.6140.5970.5310.6270.6410.6230.6350.4820.1960.4530.2960.3360.6190.2930.5541.0000.2110.0720.235
flat_type0.2780.0640.1010.1030.1350.0900.1410.1290.0990.1320.1020.6790.2890.0750.0800.1010.0180.0710.2111.0000.0730.665
storey_range0.1780.0380.0660.0650.0630.0490.0780.0700.0670.0670.0500.0550.1350.0350.0250.0590.0200.0740.0720.0731.0000.113
flat_model0.2540.1050.1710.1680.1870.1410.1890.1880.1670.2000.1360.4440.4080.0900.0900.1390.0710.1410.2350.6650.1131.000

Missing values

2023-03-18T11:50:03.689669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-18T11:50:04.842824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-18T11:50:08.170047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

resale_pricedistance_to_mrt_kmmrt_countsdistance_to_mrt_binscodes_namepopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiomale_female_ratio_binspopulation_binsmonthtownflat_typeblockstreet_namestorey_rangefloor_area_sqmflat_modellease_commence_date
0209700.01.13765110EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112001-08pasir ris4-room440pasir ris drive 401 to 03118.0model a1989
1204300.00.91622710EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112002-09pasir ris4-room473pasir ris drive 604 to 06103.0model a1989
2553500.01.34488811EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112020-10pasir risexecutive220pasir ris street 2110 to 12152.0apartment1993
3315000.00.84117210EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112010-10pasir ris4-room474pasir ris drive 604 to 06105.0model a1989
4219600.01.77480911EW36350.018880.03450.01550.06190.05320.018380.017970.00.977693102002-08pasir ris4-room751pasir ris street 7104 to 06104.0model a1996
5310500.01.15020910EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112009-04pasir ris5-room102pasir ris street 1204 to 06122.0improved1988
6333000.00.80788310EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112019-03pasir ris4-room114pasir ris street 1101 to 03106.0model a1989
7391500.00.72251410EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112017-02pasir ris4-room416pasir ris drive 607 to 09105.0model a1989
8264600.01.68908711EW36350.018880.03450.01550.06190.05320.018380.017970.00.977693102000-05pasir ris4-room746pasir ris street 7104 to 06108.0model a1996
9346500.01.15020910EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990112015-05pasir ris4-room102pasir ris street 1201 to 03104.0model a1988
resale_pricedistance_to_mrt_kmmrt_countsdistance_to_mrt_binscodes_namepopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiomale_female_ratio_binspopulation_binsmonthtownflat_typeblockstreet_namestorey_rangefloor_area_sqmflat_modellease_commence_date
539030NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102003-04bukit panjang4-room605senja road13 to 1599.0model a1999
539031NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102018-10bukit panjang5-room605senja road28 to 30120.0improved1999
539032NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102007-08bukit panjang5-room605senja road04 to 06120.0improved1999
539033NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102003-12bukit panjang4-room605senja road07 to 0999.0model a1999
539034NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102004-02bukit panjang5-room605senja road04 to 06120.0improved1999
539035NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102017-05bukit panjang4-room605senja road01 to 0399.0model a1999
539036NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102006-08bukit panjang5-room605senja road10 to 12121.0improved1999
539037NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102018-10bukit panjang5-room605senja road04 to 06120.0improved1999
539038NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102019-04bukit panjang5-room605senja road10 to 12120.0improved1999
539039NaN0.19848610BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089102004-07bukit panjang5-room605senja road04 to 06121.0improved1999

Duplicate rows

Most frequently occurring

resale_pricedistance_to_mrt_kmmrt_countsdistance_to_mrt_binscodes_namepopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiomale_female_ratio_binspopulation_binsmonthtownflat_typeblockstreet_namestorey_rangefloor_area_sqmflat_modellease_commence_date# duplicates
034200.01.21321811EW10740.05770.0960.01380.0870.01040.05360.05380.01.003731102002-01bukit merah1-room7telok blangah crescent04 to 0631.0improved19752
146800.01.21321811EW10740.05770.0960.01380.0870.01040.05360.05380.01.003731102003-08bukit merah1-room7telok blangah crescent04 to 0631.0improved19752
263000.00.36579610EW13160.06950.01120.01850.01030.01350.06950.06210.00.893525102002-10queenstown2-room64commonwealth drive01 to 0346.0standard19692
374700.00.88061710EW31120.016350.03750.01750.03720.04270.015170.015950.01.051417202002-07jurong west3-room187boon lay avenue10 to 1259.0improved19782
476500.00.43877910CC15560.07990.01150.01350.01630.02510.07960.07600.00.954774102000-08serangoon2-room8lorong lew lian10 to 1244.0improved19782
590000.00.11302610CC16000.08420.01370.01940.01410.01730.08350.07650.00.916168102004-11bukit merah2-room45telok blangah drive04 to 0645.0improved19762
690000.00.11302610CC16000.08420.01370.01940.01410.01730.08350.07650.00.916168102006-01bukit merah2-room45telok blangah drive04 to 0645.0improved19762
790000.01.47465511TE14640.07620.01100.01060.01770.02290.07180.07460.01.038997202003-01woodlands3-room217marsiling crescent01 to 0374.0model a19822
8105300.01.78373911NE39210.020100.02820.03980.04170.05650.020430.018780.00.919236102001-10serangoon3-room153serangoon north avenue 101 to 0364.0simplified19862
9108000.00.20169210NS13370.06970.0890.02050.01110.01470.07210.06160.00.854369102003-10toa payoh2-room120lorong 2 toa payoh04 to 0640.0standard19682